For all the experiments, we implement EGATs based on the Pytorch framework[1]}. Because of the large memory usage for both adjacency and mapping matrices, we convert them into sparse forms to reduce the memory requirement and computational complexity during the learning process. The experimental setup for node-sensitive and edge-sensitive graphs are described as follows.
